scikit-Learn

Linear regression can be plotted using the following libraries in Python: numpy, scipy, stats model and sckit learn.

Scikit-learn is a powerful Python module for machine learning. It contains function for regression, classification, clustering, model selection and dimensionality reduction.

• Exploring Boston Housing Data Set from scikitlearn module : import sklearn from sklearn.datasets import load_boston boston = load_boston() The object boston is a dictionary print the feature names of boston data set : print boston.feature_names

fit a linear regression model steps

1- import linear regression from sci-kit learn module : from sklearn.linear_model import LinearRegression 2- Get the data from data frame and store them as X values 3 -store linear regression object in a variable : lrm = LinearRegression()

Some of inside linear regression object functions :

lm.fit() -> fits a linear model lm.predict() -> Predict Y using the linear model with estimated coefficients lm.score() -> Returns the coefficient of determination (R^2).

train-test split : You can create training and test data sets for part od data frame manually or randomly using Scikit learn function called train_test_split 

If your graph of the data is perfect, then your data should be randomly scattered around a linear line